Image restoration by artificial intelligence

Nowadays, photo restoration is done using digital tools and software to fix any type of damage images may have and improve the general quality and definition of the details.

The ultimate goal is to enhance the visual quality, improve the interpretability, and extract relevant information from the image.

Some common methods in this domain include: This technique aims to recover the original image by estimating the inverse of the degradation function.

It helps in reducing noise, enhancing contrast, and improving image resolution for techniques such as X-ray, MRI, CT scans, and ultrasound.

Image restoration techniques are commonly used in digital photography to correct imperfections caused by factors like motion blur, lens aberrations, and sensor noise.

Image restoration plays a significant role in preserving historical documents, artworks, and photographs.

By reducing noise, enhancing faded details, and removing artifacts, valuable visual content can be preserved for future generations.

Some of the key challenges include handling complex degradations, dealing with limited information, and addressing the trade-off between restoration quality and computation time.

Convolutional neural networks (CNNs) have shown promising results in various image restoration tasks, including denoising, super-resolution, and inpainting.